Bibliographic record
Abstract
Chronic hepatitis C virus (HCV) infection-induced end-stage liver disease is the leading indication for liver transplantation and, in 2011, accounted for 1364 (23.5%) liver transplants performed in the United States. Treatment options for HCV are rapidly evolving, with realistic expectations of being able to cure the majority of patients in the very near future before the need for for transplantation arises. Until such time, the status quo we are faced with is a large cohort of HCV cirrhosis patients who will require salvage with liver transplantation. The difficulty with hepatitis C post-transplantation is that reinfection of the allograft is virtually universal. Reinfection occurs with a wide range of clinical presentations ranging from the most severe form, fibrosing cholestatic hepatitis, which occurs very early after transplantation and invariably leads to early graft failure and a possible need for retransplantation or death, to a milder but still aggressive course in the majority of patients leading to bridging fibrosis and cirrhosis. The rate at which this develops is approximately 30% to 50% at five years without antiviral treatment (1). An essential element of managing post-transplant hepatitis C is to detect individuals who are at risk of progression at an early stage, defined by most studies as a Metavir score ≥2, and commence antiviral treatment (1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".